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Found 26 papers, 16 papers with code
Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion
Our experimental evaluation shows that our method can complete the scene given a single LiDAR scan as input, producing a scene with more details compared to state-of-the-art scene completion methods.
Open-World Semantic Segmentation Including Class Similarity
Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles.
PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency
In this paper, we propose a SLAM system for building globally consistent maps, called PIN-SLAM, that is based on an elastic and compact point-based implicit neural map representation.
Unsupervised Pre-Training for 3D Leaf Instance Segmentation
Monitoring plants and measuring their traits is an important task in agriculture often referred to as plant phenotyping.
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions
Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability, reducing its impact on the environment.
Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds
In this paper, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks.
PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain
The production of food, feed, fiber, and fuel is a key task of agriculture.
On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics
Furthermore, the pre-trained networks obtain similar performance to the fully supervised with less labeled data.
Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization
We exploit 3D object detections from monocular RGB frames for both, the object-based map construction, and for globally localizing in the constructed map.
Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots
Monitoring plants and fruits at high resolution play a key role in the future of agriculture.
Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving
Especially in autonomous driving, point clouds are sparse, and objects appearance depends on its distance from the sensor, making it harder to acquire large amounts of labeled training data.
Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data
Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning.
Fully On-board Low-Power Localization with Multizone Time-of-Flight Sensors on Nano-UAVs
Experimental evaluation using a nano-UAV open platform demonstrated that the proposed solution is capable of localizing on a 31. 2m$\boldsymbol{^2}$ map with 0. 15m accuracy and an above 95% success rate.
Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
In this paper, we address the problem of joint semantic, plant instance, and leaf instance segmentation of crop fields from RGB data.
IR-MCL: Implicit Representation-Based Online Global Localization
The experiments suggest that the presented implicit representation is able to predict more accurate 2D LiDAR scans leading to an improved observation model for our particle filter-based localization.
Robust Double-Encoder Network for RGB-D Panoptic Segmentation
We propose a novel encoder-decoder neural network that processes RGB and depth separately through two encoders.
SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations
Accurate mapping of large-scale environments is an essential building block of most outdoor autonomous systems.
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments.
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks
In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans.
4D Panoptic LiDAR Segmentation
In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points.
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI
Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly.
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities.
Ranked #19 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals
For localization and mapping, we employ an efficient direct tracking on the truncated signed distance function (TSDF) and leverage color information encoded in the TSDF to estimate the pose of the sensor.
Robotics
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Ranked #32 on
3D Semantic Segmentation
on SemanticKITTI
Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming
Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i. e., a semantic segmentation.
Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming
It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying.
